Astrophysics > High Energy Astrophysical Phenomena

Title:
Classifying X-ray Binaries: A Probabilistic Approach

Abstract: In X-ray binary star systems consisting of a compact object that accretes
material from an orbiting secondary star, there is no straightforward means to
decide if the compact object is a black hole or a neutron star. To assist this
classification, we develop a Bayesian statistical model that makes use of the
fact that X-ray binary systems appear to cluster based on their compact object
type when viewed from a 3-dimensional coordinate system derived from X-ray
spectral data. The first coordinate of this data is the ratio of counts in mid
to low energy band (color 1), the second coordinate is the ratio of counts in
high to low energy band (color 2), and the third coordinate is the sum of
counts in all three bands. We use this model to estimate the probabilities that
an X-ray binary system contains a black hole, non-pulsing neutron star, or
pulsing neutron star. In particular, we utilize a latent variable model in
which the latent variables follow a Gaussian process prior distribution, and
hence we are able to induce the spatial correlation we believe exists between
systems of the same type. The utility of this approach is evidenced by the
accurate prediction of system types using Rossi X-ray Timing Explorer All Sky
Monitor data, but it is not flawless. In particular, non-pulsing neutron
systems containing "bursters" that are close to the boundary demarcating
systems containing black holes tend to be classified as black hole systems. As
a byproduct of our analyses, we provide the astronomer with public R code that
can be used to predict the compact object type of X-ray binaries given training
data.